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Mid-level, popular Data Engineer title plus broad skillset and recognizable employer increases applicant competition.
Core data engineering skills (Spark, SQL, Python, ETL) transfer easily across industries with minimal domain lock-in.
Explicit 2-5 years requirement plus mandatory Spark/Databricks/Azure/Python/SQL stack increases filtering strictness.
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Owns design, development, and maintenance of marketing analytics data ecosystem including data ingestion pipelines and processing using big data technologies.
Collaborates with stakeholders to translate business requirements into analytical tools, dashboards, and data workflows, ensuring usability and data quality.
Supports proof of concept projects, automates data processes, troubleshoots data and analytics issues, and manages back-end data engineering tasks using Hadoop, Spark, SQL, and cloud services.
Bachelor’s Degree in Computer Science, Engineering or related field.
2 to 5+ years of work experience in Data & Analytics roles.
Mandatory technical skills include Python, SQL, Spark, Databricks, ETL, Hadoop ecosystem (Hive, HDFS, Sqoop).
Not explicitly mentioned: notice period requirement.
Experienced data engineer with strong expertise in big data frameworks (Hadoop, Spark) and cloud data services (Azure Data Factory, Databricks).
Proven ability to collaborate with cross-functional teams including business analysts and technical architects to deliver usable analytics solutions.
Capable of handling multiple concurrent projects, building POCs for streaming data ingestion and processing, and automating data workflows for efficiency.